In Advanced materials (Deerfield Beach, Fla.)
Current functional assessment of biomaterial-induced stem cell lineage fate in vitro mainly relies on biomarker-dependent methods with limited accuracy and efficiency. Here we report a "Mesenchymal stem cell Differentiation Prediction (MeD-P)" framework for biomaterial-induced cell lineage fate prediction. MeD-P contains a cell-type-specific gene expression profile as a reference by integrating public RNA-seq data related to tri-lineage differentiation (osteogenesis, chondrogenesis, and adipogenesis) of human mesenchymal stem cells (hMSCs) and a predictive model for classifying hMSCs differentiation lineages using the k-nearest neighbors (kNN) strategy. We show that MeD-P exhibits an overall accuracy of 90.63% on testing datasets, which is significantly higher than the model constructed based on canonical marker genes (80.21%). Moreover, evaluations of multiple biomaterials show that MeD-P provides accurate prediction of lineage fate on different types of biomaterials as early as the first week of hMSCs culture. In summary, we demonstrate that MeD-P is an efficient and accurate strategy for stem cell lineage fate prediction and preliminary biomaterial functional evaluation. This article is protected by copyright. All rights reserved.
Zhou Yingying, Ping Xianfeng, Guo Yusi, Heng Boon Chin, Wang Yijun, Meng Yanze, Jiang Shengjie, Wei Yan, Lai Binbin, Zhang Xuehui, Deng Xuliang
2023-Feb-09
artificial intelligence, gene expression pattern, lineage fate, machine learning, mesenchymal stem cells, regenerative biomaterials
